7 Ways to Leverage Data Science and ML-Backed Solutions for Your Business
Data Science: The success formula
This is undoubtedly true that data science has burst into IT markets during the recent years. It is predicted to become one of the most widely-used methodologies the aim of which is to cope with the exponentially growing amount of information in the world.
Have you ever thought how much data is being produced this very moment? The 2018 numbers are thrilling: 2800 smartphones are sold, almost 50 thousand photos are posted on Instagram, around 4 million searches are conducted by Google, more than 18 million people request for weather forecast every minute. But what is more essential is what companies do with this information.
Data-driven companies are now learning to benefit from data science in more effective ways. This system comprises complex mathematical strategies, a variety of statistical techniques, and numerous branches of predictive analytics - from data mining to machine learning (ML). In other words, data science enables to gather, study, cleanse, sort out and analyze both structured and unstructured data in order to extract business value and recommend far-reaching moves. Competent data-based decisions are pivotal to any enterprise, especially in the era of likes, tweets, clicks and shares.
Meanwhile, machine learning gains general recognition as a part of artificial intelligence (AI) technique. ML, alongside with data science, results in business intelligence – the ability to predict the future in the world of marketplace. According to recent results of HFS Research, 86 % of leading enterprises agree that ML is influencing their industries in general and companies in particular. It is also stated in the report that more than 75 % of decision-makers positively appraise the business value of Machine Learning. On top of this, this study indicates the forthcoming mass ML acceptance (52 % of companies) within the next two years.
7 Data Science and Machine Learning benefits
“Think of big data as an epic wave gathering now, starting to crest. If you want to catch it, you need people who can surf.” – Harvard Business Review
Here are some reasons that make data science and ML decisions such a powerful leverage for the business industry.
- Multiple data sources processing
- Real-time demand and supply
- Meeting customers’ needs
- Improved decision making
- Fraud detection and risk diminishing
- Talented dream team
- Technology and people cooperation
In comparison to traditional systems, data analytics is able to handle diverse continuous streams of unstructured data in real time. ML technology embraces the information from the internet, third-party sources, mobile applications on smartphones, cloud storages, social networks, etc. The data volume is enormous and ML is a superpower here: it results in deep data analysis for better positive outcomes for both companies and customers.
It is inevitable for every enterprise to clearly understand the anticipated outcomes of data mining conducted. Taking into consideration the on-going demand for definite products, companies evaluate whether they are able to provide the supply at stake. Monitoring online ads, visited pages or saved links enables to track the customers’ quickly-changing digital footprint and to shift to new market trends as soon as possible.
Automated ML solutions can prove beneficial in product recommendations. Data analytics makes it possible to fight for each customer through a personalized approach. Meanwhile, AI is studying personalities, locations, interactions and experiences of users, the company is busy satisfying the customers’ real-time expectations, making them feel personally valued. Isn’t it pleasing when Youtube suggests the newest song of your favorite singer?
Once the abilities are understood, the attention shifts to the decision making. Business performance can be forecast with the usage of ML-backed solutions. In other words, computers are able to analyze the forthcoming results of any decision made. Companies now are more protected from failure if their choices are based on measurable, data-driven knowledge. More to say, for data science it is casual to quantify the results of each decision implementation, to test the changes and visualize the success.
Privacy is the core essence of longstanding relationships with a customer, that’s why every company is fully responsible for data safety. Security analytics targets to ensure fraud detection and prevent unusual activity in order to protect personal data from misusage. The great example of such protection is face and voice recognition most widely deployed in smartphones. Another ML-based practice to mitigate risks is email spam filters and special credit card authorization while online shopping.
Data scientists are universal soldiers who orchestrate difficult data processes. These professionals are a combination of profound education in mathematics, statistics, computer science or data analytics. They are also characterized by constant curiosity, problem-solving and critical thinking, communication skills, creativity, skepticism and intuition. Data scientists dive into the data lakes, distinguish valuable and obscure data sources, fish out precious information, visualize and deliver it to product managers, executives and stakeholders. Easy to say, difficult to find. This talent gap gives rise to the partnership between advanced ML companies and engineering schools with benefits for both sides. In addition, data scientists will be of great demand in upcoming years.
Data science, AI and ML have caught up and reached far beyond human abilities. Computers can conduct numerous people’s functions: driving cars, recognizing voices, faces, fingerprints, doing sums and translating texts, cooking and cleaning, doing shopping, etc. In business ML strategies focus on facts, machines substitute human mind, they are free from judgments and emotions, act logically, are multifaceted and times quicker than people. Nevertheless, we speak about the tight cooperation between machines and data specialists, as people’s task is to program and train computers to think, to make necessary decisions and to predict desirable outcomes. Machines are in charge of conducting calculating, analyzing, detecting, etc., meanwhile, data specialists are responsible for writing algorithms, guiding the processes and taking actions.
All in all, the ability to deploy data science methods and ML solutions in business can result in prosperous outcomes for everyone who dares to invest in it right away.
Feel free to firstname.lastname@example.org in case you need more insights on how to integrate ML and Data science solutions into your business.